1. Field
The disclosure generally relates to mobile recommendation systems and methods for providing personal touring services.
2. Description of the Related Art
With the advancements in wireless communication technologies and related products and applications, most countries are aiming to build a ubiquitous network society, where people may access various services and resources on the Internet anytime and anywhere, through the use of digital consumer products with wireless communication capabilities. In addition, mobile service applications are expected to be premium quality. In an exhibition, visitors always want to visit each interesting exhibit in a limited amount of time. On the other hand, exhibitors want to have as many visitors as possible to their stands and to increase the amount of finished deals during the exhibition period. Personalized recommendation sorts out the stands (or products) which a specific visitor may be interested in among all the stands in an exhibition, according to the preference of the specific visitor. A recommendation list is thus generated by further organizing the sorted stands in a preferable order for the specific visitor. The personalized recommendation techniques are highly valued in pairing up the visitors and the exhibitors and effectively bringing good business results for both sides, especially in busy situations where time is limited.
Generally, conventional recommendation techniques may be categorized into content-based recommendation techniques and collaborative recommendation techniques. In the content-based recommendation techniques, a recommendation is generated by considering the relevance between the objects according to the content of the objects, such as the names, the classifications, the manufacturers, and the places of origins of the objects. In the collaborative recommendation techniques, the quantification values, such as the patterns and types of purchases, given by the users are taken as evaluation standards by which to determine the preferences of the users, and a recommendation is generated according to the determined preferences of the users. However, when applied in a real exhibition or megastore environment, the number of objects to be evaluated is great. In addition, to have users provide the quantification values of each object is a time-consuming process and impractical. Thus, the conventional content-based recommendation and collaborative recommendation methods are not suitable for real exhibitions or megastore environments.